⚡AgentSkills
🧮 ML Research Engineering · Experimentation Rigor

Keep experiment tracking trustworthy

Every run reproducible from logged config + code version + data snapshot reference.

foundation~30 minResearch EngineersML ScientistsPhD Researchers

Steps

  1. 1Log config automatically from source of truth, never hand-transcribed
  2. 2Attach git SHA + diff for uncommitted changes to every run
  3. 3Reference datasets by content hash or immutable version tag
  4. 4Name runs by hypothesis ID, not creative adjectives
  5. 5Tag runs: baseline / candidate / aborted / champion with promotion reasons
  6. 6Weekly review: kill zombie experiments, archive stale branches

Common Pitfalls

  • ▲Best result ever that nobody can rerun
  • ▲Config drift between what ran and what was documented

Commands

Install with skills CLI
$ npx skills add aniruddhaadak80/skills --skill experimentation-rigor-experiment-tracking-hygiene
Install globally
$ npx skills add aniruddhaadak80/skills --skill experimentation-rigor-experiment-tracking-hygiene -g

Tags

#tracking#mlops#reproducibility#ml-research#experimentation-rigor

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